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pandas data frame, apply t-test on rows simultaneously grouping by column names (have duplicates!)

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0

I have a data frame with particular readouts as indexes (different types of measurements for a given sample), each column is a sample for which these readouts were taken. I also have a treatment group assigned as the column name for each sample. You can see the example below.

What I need to do: for a given readout (row) group samples by treatment (column name) and perform a t-test (Welch's t-test) on each group (each treatment). T-test must be done as a comparison with one fixed treatment (control treatment). I do not care about tracking out the sample ids (it was required, now I dropped them on purpose), I'm not going to do paired tests.

For example here, for readout1 I need to compare treatment1 vs treatment3, treatment2 vs treatment3 (it's ok if I'll also get treatment3 vs treatment3).

Example of data frame:

frame = pd.DataFrame(np.arange(27).reshape((3, 9)),
 index=['readout1', 'readout2', 'readout3'],
 columns=['treatment1', 'treatment1', 'treatment1',
 'treatment2', 'treatment2', 'treatment2', 
 'treatment3', 'treatment3', 'treatment3'])

frame
Out[757]: 
 treatment1 treatment1 ... treatment3 treatment3
readout1 0 1 ... 7 8
readout2 9 10 ... 16 17
readout3 18 19 ... 25 26

[3 rows x 9 columns]

I'm fighting it for several days now. Tried to unstack/stack the data, transposing the data frame, then grouping by index, removing nan and applying lambda. Tried other strategies but none worked. Will appreciate any help.
thank you!

python pandas aggregate apply

share|improve this question

asked Mar 9 at 6:04

rakshyrakshy

13

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  just guessing if you print 'df'+str(e) : frame.loc[:,i] for e,i in enumerate(list(set(itertools.combinations(frame.columns,2)))) so you get the desired combinations in a dict? import itertools first
  
  – anky_91
  Mar 9 at 6:13
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  thanks, I did end up just transforming the table into a dictionary. I just hoped that there is a more elegant way to do it
  
  – rakshy
  Mar 15 at 1:08
  
  

  
  
  
  

add a comment | 

0

I have a data frame with particular readouts as indexes (different types of measurements for a given sample), each column is a sample for which these readouts were taken. I also have a treatment group assigned as the column name for each sample. You can see the example below.

What I need to do: for a given readout (row) group samples by treatment (column name) and perform a t-test (Welch's t-test) on each group (each treatment). T-test must be done as a comparison with one fixed treatment (control treatment). I do not care about tracking out the sample ids (it was required, now I dropped them on purpose), I'm not going to do paired tests.

For example here, for readout1 I need to compare treatment1 vs treatment3, treatment2 vs treatment3 (it's ok if I'll also get treatment3 vs treatment3).

Example of data frame:

frame = pd.DataFrame(np.arange(27).reshape((3, 9)),
 index=['readout1', 'readout2', 'readout3'],
 columns=['treatment1', 'treatment1', 'treatment1',
 'treatment2', 'treatment2', 'treatment2', 
 'treatment3', 'treatment3', 'treatment3'])

frame
Out[757]: 
 treatment1 treatment1 ... treatment3 treatment3
readout1 0 1 ... 7 8
readout2 9 10 ... 16 17
readout3 18 19 ... 25 26

[3 rows x 9 columns]

I'm fighting it for several days now. Tried to unstack/stack the data, transposing the data frame, then grouping by index, removing nan and applying lambda. Tried other strategies but none worked. Will appreciate any help.
thank you!

python pandas aggregate apply

share|improve this question

asked Mar 9 at 6:04

rakshyrakshy

13

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  just guessing if you print 'df'+str(e) : frame.loc[:,i] for e,i in enumerate(list(set(itertools.combinations(frame.columns,2)))) so you get the desired combinations in a dict? import itertools first
  
  – anky_91
  Mar 9 at 6:13
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  thanks, I did end up just transforming the table into a dictionary. I just hoped that there is a more elegant way to do it
  
  – rakshy
  Mar 15 at 1:08
  
  

  
  
  
  

add a comment | 

I have a data frame with particular readouts as indexes (different types of measurements for a given sample), each column is a sample for which these readouts were taken. I also have a treatment group assigned as the column name for each sample. You can see the example below.

What I need to do: for a given readout (row) group samples by treatment (column name) and perform a t-test (Welch's t-test) on each group (each treatment). T-test must be done as a comparison with one fixed treatment (control treatment). I do not care about tracking out the sample ids (it was required, now I dropped them on purpose), I'm not going to do paired tests.

For example here, for readout1 I need to compare treatment1 vs treatment3, treatment2 vs treatment3 (it's ok if I'll also get treatment3 vs treatment3).

Example of data frame:

frame = pd.DataFrame(np.arange(27).reshape((3, 9)),
 index=['readout1', 'readout2', 'readout3'],
 columns=['treatment1', 'treatment1', 'treatment1',
 'treatment2', 'treatment2', 'treatment2', 
 'treatment3', 'treatment3', 'treatment3'])

frame
Out[757]: 
 treatment1 treatment1 ... treatment3 treatment3
readout1 0 1 ... 7 8
readout2 9 10 ... 16 17
readout3 18 19 ... 25 26

[3 rows x 9 columns]

I'm fighting it for several days now. Tried to unstack/stack the data, transposing the data frame, then grouping by index, removing nan and applying lambda. Tried other strategies but none worked. Will appreciate any help.
thank you!

python pandas aggregate apply

share|improve this question

asked Mar 9 at 6:04

rakshyrakshy

13

frame = pd.DataFrame(np.arange(27).reshape((3, 9)),
 index=['readout1', 'readout2', 'readout3'],
 columns=['treatment1', 'treatment1', 'treatment1',
 'treatment2', 'treatment2', 'treatment2', 
 'treatment3', 'treatment3', 'treatment3'])

frame
Out[757]: 
 treatment1 treatment1 ... treatment3 treatment3
readout1 0 1 ... 7 8
readout2 9 10 ... 16 17
readout3 18 19 ... 25 26

[3 rows x 9 columns]

frame
Out[757]: 
 treatment1 treatment1 ... treatment3 treatment3
readout1 0 1 ... 7 8
readout2 9 10 ... 16 17
readout3 18 19 ... 25 26

[3 rows x 9 columns]

frame
Out[757]: 
 treatment1 treatment1 ... treatment3 treatment3
readout1 0 1 ... 7 8
readout2 9 10 ... 16 17
readout3 18 19 ... 25 26

[3 rows x 9 columns]

share|improve this question

asked Mar 9 at 6:04

rakshyrakshy

13

share|improve this question

asked Mar 9 at 6:04

rakshyrakshy

13

asked Mar 9 at 6:04

rakshyrakshy

13

asked Mar 9 at 6:04

rakshyrakshy

13